Research on battery remaining life prediction algorithm for portable medical devices
10.19745/j.1003-8868.2024148
- VernacularTitle:便携式医疗设备电池剩余寿命预测算法研究
- Author:
Lei SHI
1
;
Dai-Ning AN
;
Peng-Fei GAO
Author Information
1. 河北省产业转型升级服务中心,石家庄 050051
- Keywords:
portable medical device;
battery remaining life;
BP neural network;
sparrow search algorithm
- From:
Chinese Medical Equipment Journal
2024;45(8):21-25
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a SSA-BP algorithm based on the back propagation(BP)neural network and sparrow search algorithm(SSA)to predict battery remaining life accurately.Methods Firstly,the total number of the weights and thresholds was determined with the structure of the BP neural network;secondly,the initial weights and thresholds were optimized using the SSA algorithm and assigned to the BP neural network;and finally,the predicted output values were obtained by training the input samples.The data of 18650 model lithium batteries at different ambient temperatures(4,24,43 ℃)were selected for testing,and the prediction accuracy of the SSA-BP neural network algorithm and BP neural network algorithm on the remaining life of medical device batteries was verified by the mean absolute error,root mean square error and mean absolute percentage error.Results The SSA-BP algorithm had the average absolute error,root mean square error and mean absolute percentage error lower than those of the BP neural network when used to predict battery remaining life.Conclusion The SSA-BP algorithm can effectively predict battery remaining life,and enhances battery reliability during practical application.[Chinese Medical Equipment Journal,2024,45(8):21-25]
